Overview

Dataset statistics

Number of variables18
Number of observations418
Missing cells845
Missing cells (%)11.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory276.7 KiB
Average record size in memory677.9 B

Variable types

Numeric6
Unsupported1
Categorical8
Text3

Alerts

DatasetName has constant value ""Constant
Survived has 418 (100.0%) missing valuesMissing
Age has 86 (20.6%) missing valuesMissing
Cabin has 327 (78.2%) missing valuesMissing
CabinPrefix has 13 (3.1%) missing valuesMissing
PassengerId is uniformly distributedUniform
PassengerId has unique valuesUnique
Name has unique valuesUnique
Survived is an unsupported type, check if it needs cleaning or further analysisUnsupported
SibSp has 283 (67.7%) zerosZeros
Parch has 324 (77.5%) zerosZeros

Reproduction

Analysis started2024-03-23 19:04:22.750871
Analysis finished2024-03-23 19:04:28.336937
Duration5.59 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100.5
Minimum892
Maximum1309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-23T16:04:28.448639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum892
5-th percentile912.85
Q1996.25
median1100.5
Q31204.75
95-th percentile1288.15
Maximum1309
Range417
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation120.81046
Coefficient of variation (CV)0.10977779
Kurtosis-1.2
Mean1100.5
Median Absolute Deviation (MAD)104.5
Skewness0
Sum460009
Variance14595.167
MonotonicityStrictly increasing
2024-03-23T16:04:28.643152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
892 1
 
0.2%
1205 1
 
0.2%
1177 1
 
0.2%
1176 1
 
0.2%
1175 1
 
0.2%
1174 1
 
0.2%
1173 1
 
0.2%
1172 1
 
0.2%
1171 1
 
0.2%
1170 1
 
0.2%
Other values (408) 408
97.6%
ValueCountFrequency (%)
892 1
0.2%
893 1
0.2%
894 1
0.2%
895 1
0.2%
896 1
0.2%
897 1
0.2%
898 1
0.2%
899 1
0.2%
900 1
0.2%
901 1
0.2%
ValueCountFrequency (%)
1309 1
0.2%
1308 1
0.2%
1307 1
0.2%
1306 1
0.2%
1305 1
0.2%
1304 1
0.2%
1303 1
0.2%
1302 1
0.2%
1301 1
0.2%
1300 1
0.2%

Survived
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing418
Missing (%)100.0%
Memory size3.4 KiB

Pclass
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
3
218 
1
107 
2
93 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Length

2024-03-23T16:04:28.800696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:28.962296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring characters

ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Name
Text

UNIQUE 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
2024-03-23T16:04:29.204618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length63
Median length51
Mean length27.483254
Min length13

Characters and Unicode

Total characters11488
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique418 ?
Unique (%)100.0%

Sample

1st rowKelly, Mr. James
2nd rowWilkes, Mrs. James (Ellen Needs)
3rd rowMyles, Mr. Thomas Francis
4th rowWirz, Mr. Albert
5th rowHirvonen, Mrs. Alexander (Helga E Lindqvist)
ValueCountFrequency (%)
mr 242
 
14.0%
miss 78
 
4.5%
mrs 72
 
4.2%
john 28
 
1.6%
william 23
 
1.3%
master 21
 
1.2%
charles 16
 
0.9%
joseph 15
 
0.9%
james 14
 
0.8%
henry 14
 
0.8%
Other values (825) 1202
69.7%
2024-03-23T16:04:29.828979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1309
 
11.4%
r 971
 
8.5%
e 822
 
7.2%
a 786
 
6.8%
s 628
 
5.5%
i 621
 
5.4%
n 596
 
5.2%
l 526
 
4.6%
M 515
 
4.5%
o 467
 
4.1%
Other values (48) 4247
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7395
64.4%
Uppercase Letter 1738
 
15.1%
Space Separator 1309
 
11.4%
Other Punctuation 884
 
7.7%
Open Punctuation 78
 
0.7%
Close Punctuation 78
 
0.7%
Dash Punctuation 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 971
13.1%
e 822
11.1%
a 786
10.6%
s 628
8.5%
i 621
8.4%
n 596
8.1%
l 526
 
7.1%
o 467
 
6.3%
t 303
 
4.1%
h 257
 
3.5%
Other values (16) 1418
19.2%
Uppercase Letter
ValueCountFrequency (%)
M 515
29.6%
J 112
 
6.4%
A 103
 
5.9%
C 101
 
5.8%
E 95
 
5.5%
S 81
 
4.7%
H 80
 
4.6%
W 76
 
4.4%
B 69
 
4.0%
L 61
 
3.5%
Other values (14) 445
25.6%
Other Punctuation
ValueCountFrequency (%)
. 418
47.3%
, 418
47.3%
" 44
 
5.0%
' 4
 
0.5%
Space Separator
ValueCountFrequency (%)
1309
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9133
79.5%
Common 2355
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 971
 
10.6%
e 822
 
9.0%
a 786
 
8.6%
s 628
 
6.9%
i 621
 
6.8%
n 596
 
6.5%
l 526
 
5.8%
M 515
 
5.6%
o 467
 
5.1%
t 303
 
3.3%
Other values (40) 2898
31.7%
Common
ValueCountFrequency (%)
1309
55.6%
. 418
 
17.7%
, 418
 
17.7%
( 78
 
3.3%
) 78
 
3.3%
" 44
 
1.9%
- 6
 
0.3%
' 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1309
 
11.4%
r 971
 
8.5%
e 822
 
7.2%
a 786
 
6.8%
s 628
 
5.5%
i 621
 
5.4%
n 596
 
5.2%
l 526
 
4.6%
M 515
 
4.5%
o 467
 
4.1%
Other values (48) 4247
37.0%

Sex
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
male
266 
female
152 

Length

Max length6
Median length4
Mean length4.7272727
Min length4

Characters and Unicode

Total characters1976
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowfemale

Common Values

ValueCountFrequency (%)
male 266
63.6%
female 152
36.4%

Length

2024-03-23T16:04:30.009465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:30.153081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
male 266
63.6%
female 152
36.4%

Most occurring characters

ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1976
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1976
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Age
Real number (ℝ)

MISSING 

Distinct79
Distinct (%)23.8%
Missing86
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean30.27259
Minimum0.17
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-23T16:04:30.311656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile8
Q121
median27
Q339
95-th percentile57
Maximum76
Range75.83
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.181209
Coefficient of variation (CV)0.46845047
Kurtosis0.083783352
Mean30.27259
Median Absolute Deviation (MAD)8
Skewness0.45736129
Sum10050.5
Variance201.1067
MonotonicityNot monotonic
2024-03-23T16:04:30.517137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 17
 
4.1%
21 17
 
4.1%
22 16
 
3.8%
30 15
 
3.6%
18 13
 
3.1%
27 12
 
2.9%
26 12
 
2.9%
23 11
 
2.6%
25 11
 
2.6%
29 10
 
2.4%
Other values (69) 198
47.4%
(Missing) 86
20.6%
ValueCountFrequency (%)
0.17 1
 
0.2%
0.33 1
 
0.2%
0.75 1
 
0.2%
0.83 1
 
0.2%
0.92 1
 
0.2%
1 3
0.7%
2 2
0.5%
3 1
 
0.2%
5 1
 
0.2%
6 3
0.7%
ValueCountFrequency (%)
76 1
 
0.2%
67 1
 
0.2%
64 3
0.7%
63 2
0.5%
62 1
 
0.2%
61 2
0.5%
60.5 1
 
0.2%
60 3
0.7%
59 1
 
0.2%
58 1
 
0.2%

SibSp
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44736842
Minimum0
Maximum8
Zeros283
Zeros (%)67.7%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-23T16:04:30.688650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89675956
Coefficient of variation (CV)2.0045214
Kurtosis26.498712
Mean0.44736842
Median Absolute Deviation (MAD)0
Skewness4.1683366
Sum187
Variance0.80417771
MonotonicityNot monotonic
2024-03-23T16:04:30.855240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 283
67.7%
1 110
 
26.3%
2 14
 
3.3%
3 4
 
1.0%
4 4
 
1.0%
8 2
 
0.5%
5 1
 
0.2%
ValueCountFrequency (%)
0 283
67.7%
1 110
 
26.3%
2 14
 
3.3%
3 4
 
1.0%
4 4
 
1.0%
5 1
 
0.2%
8 2
 
0.5%
ValueCountFrequency (%)
8 2
 
0.5%
5 1
 
0.2%
4 4
 
1.0%
3 4
 
1.0%
2 14
 
3.3%
1 110
 
26.3%
0 283
67.7%

Parch
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3923445
Minimum0
Maximum9
Zeros324
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-23T16:04:31.022771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98142888
Coefficient of variation (CV)2.5014468
Kurtosis31.412513
Mean0.3923445
Median Absolute Deviation (MAD)0
Skewness4.6544617
Sum164
Variance0.96320264
MonotonicityNot monotonic
2024-03-23T16:04:31.161412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 324
77.5%
1 52
 
12.4%
2 33
 
7.9%
3 3
 
0.7%
4 2
 
0.5%
9 2
 
0.5%
6 1
 
0.2%
5 1
 
0.2%
ValueCountFrequency (%)
0 324
77.5%
1 52
 
12.4%
2 33
 
7.9%
3 3
 
0.7%
4 2
 
0.5%
5 1
 
0.2%
6 1
 
0.2%
9 2
 
0.5%
ValueCountFrequency (%)
9 2
 
0.5%
6 1
 
0.2%
5 1
 
0.2%
4 2
 
0.5%
3 3
 
0.7%
2 33
 
7.9%
1 52
 
12.4%
0 324
77.5%

Ticket
Text

Distinct363
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
2024-03-23T16:04:31.434654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.8755981
Min length3

Characters and Unicode

Total characters2874
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique321 ?
Unique (%)76.8%

Sample

1st row330911
2nd row363272
3rd row240276
4th row315154
5th row3101298
ValueCountFrequency (%)
pc 32
 
5.9%
c.a 19
 
3.5%
ca 8
 
1.5%
soton/o.q 8
 
1.5%
sc/paris 7
 
1.3%
17608 5
 
0.9%
2 5
 
0.9%
a/5 5
 
0.9%
w./c 5
 
0.9%
f.c.c 4
 
0.7%
Other values (383) 445
82.0%
2024-03-23T16:04:31.966233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 364
12.7%
1 311
10.8%
2 268
9.3%
7 207
 
7.2%
6 206
 
7.2%
0 204
 
7.1%
5 195
 
6.8%
4 188
 
6.5%
8 144
 
5.0%
9 137
 
4.8%
Other values (22) 650
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2224
77.4%
Uppercase Letter 349
 
12.1%
Other Punctuation 172
 
6.0%
Space Separator 125
 
4.3%
Lowercase Letter 4
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 92
26.4%
P 52
14.9%
A 51
14.6%
O 44
12.6%
S 40
11.5%
T 14
 
4.0%
N 14
 
4.0%
Q 12
 
3.4%
R 7
 
2.0%
I 7
 
2.0%
Other values (5) 16
 
4.6%
Decimal Number
ValueCountFrequency (%)
3 364
16.4%
1 311
14.0%
2 268
12.1%
7 207
9.3%
6 206
9.3%
0 204
9.2%
5 195
8.8%
4 188
8.5%
8 144
 
6.5%
9 137
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
a 1
25.0%
r 1
25.0%
i 1
25.0%
s 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 126
73.3%
/ 46
 
26.7%
Space Separator
ValueCountFrequency (%)
125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2521
87.7%
Latin 353
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 92
26.1%
P 52
14.7%
A 51
14.4%
O 44
12.5%
S 40
11.3%
T 14
 
4.0%
N 14
 
4.0%
Q 12
 
3.4%
R 7
 
2.0%
I 7
 
2.0%
Other values (9) 20
 
5.7%
Common
ValueCountFrequency (%)
3 364
14.4%
1 311
12.3%
2 268
10.6%
7 207
8.2%
6 206
8.2%
0 204
8.1%
5 195
7.7%
4 188
7.5%
8 144
 
5.7%
9 137
 
5.4%
Other values (3) 297
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 364
12.7%
1 311
10.8%
2 268
9.3%
7 207
 
7.2%
6 206
 
7.2%
0 204
 
7.1%
5 195
 
6.8%
4 188
 
6.5%
8 144
 
5.0%
9 137
 
4.8%
Other values (22) 650
22.6%

Fare
Real number (ℝ)

Distinct169
Distinct (%)40.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean35.627188
Minimum0
Maximum512.3292
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-23T16:04:32.181657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.2292
Q17.8958
median14.4542
Q331.5
95-th percentile151.55
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.6042

Descriptive statistics

Standard deviation55.907576
Coefficient of variation (CV)1.5692391
Kurtosis17.921595
Mean35.627188
Median Absolute Deviation (MAD)6.825
Skewness3.6872133
Sum14856.538
Variance3125.6571
MonotonicityNot monotonic
2024-03-23T16:04:32.353199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.75 21
 
5.0%
26 19
 
4.5%
8.05 17
 
4.1%
13 17
 
4.1%
10.5 11
 
2.6%
7.8958 11
 
2.6%
7.775 10
 
2.4%
7.2292 9
 
2.2%
7.225 9
 
2.2%
7.8542 8
 
1.9%
Other values (159) 285
68.2%
ValueCountFrequency (%)
0 2
 
0.5%
3.1708 1
 
0.2%
6.4375 2
 
0.5%
6.4958 1
 
0.2%
6.95 1
 
0.2%
7 2
 
0.5%
7.05 2
 
0.5%
7.225 9
2.2%
7.2292 9
2.2%
7.25 5
1.2%
ValueCountFrequency (%)
512.3292 1
 
0.2%
263 2
 
0.5%
262.375 5
1.2%
247.5208 1
 
0.2%
227.525 1
 
0.2%
221.7792 3
0.7%
211.5 4
1.0%
211.3375 1
 
0.2%
164.8667 2
 
0.5%
151.55 2
 
0.5%

Cabin
Text

MISSING 

Distinct76
Distinct (%)83.5%
Missing327
Missing (%)78.2%
Memory size15.8 KiB
2024-03-23T16:04:32.562639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length3
Mean length4.0769231
Min length1

Characters and Unicode

Total characters371
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)68.1%

Sample

1st rowB45
2nd rowE31
3rd rowB57 B59 B63 B66
4th rowB36
5th rowA21
ValueCountFrequency (%)
f 4
 
3.4%
b57 3
 
2.5%
b63 3
 
2.5%
b66 3
 
2.5%
b59 3
 
2.5%
c27 2
 
1.7%
e46 2
 
1.7%
c6 2
 
1.7%
c78 2
 
1.7%
b45 2
 
1.7%
Other values (80) 92
78.0%
2024-03-23T16:04:32.916693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 43
11.6%
5 34
9.2%
1 33
 
8.9%
B 32
 
8.6%
6 30
 
8.1%
3 28
 
7.5%
27
 
7.3%
2 25
 
6.7%
4 21
 
5.7%
7 15
 
4.0%
Other values (8) 83
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 226
60.9%
Uppercase Letter 118
31.8%
Space Separator 27
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 34
15.0%
1 33
14.6%
6 30
13.3%
3 28
12.4%
2 25
11.1%
4 21
9.3%
7 15
6.6%
8 14
6.2%
0 14
6.2%
9 12
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
C 43
36.4%
B 32
27.1%
D 14
 
11.9%
E 12
 
10.2%
F 8
 
6.8%
A 7
 
5.9%
G 2
 
1.7%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 253
68.2%
Latin 118
31.8%

Most frequent character per script

Common
ValueCountFrequency (%)
5 34
13.4%
1 33
13.0%
6 30
11.9%
3 28
11.1%
27
10.7%
2 25
9.9%
4 21
8.3%
7 15
5.9%
8 14
5.5%
0 14
5.5%
Latin
ValueCountFrequency (%)
C 43
36.4%
B 32
27.1%
D 14
 
11.9%
E 12
 
10.2%
F 8
 
6.8%
A 7
 
5.9%
G 2
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 43
11.6%
5 34
9.2%
1 33
 
8.9%
B 32
 
8.6%
6 30
 
8.1%
3 28
 
7.5%
27
 
7.3%
2 25
 
6.7%
4 21
 
5.7%
7 15
 
4.0%
Other values (8) 83
22.4%

Embarked
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
S
270 
C
102 
Q
46 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ
2nd rowS
3rd rowQ
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Length

2024-03-23T16:04:33.069316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:33.195978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
s 270
64.6%
c 102
 
24.4%
q 46
 
11.0%

Most occurring characters

ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 418
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 418
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

DatasetName
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
test
418 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1672
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtest
2nd rowtest
3rd rowtest
4th rowtest
5th rowtest

Common Values

ValueCountFrequency (%)
test 418
100.0%

Length

2024-03-23T16:04:33.330616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:33.445308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
test 418
100.0%

Most occurring characters

ValueCountFrequency (%)
t 836
50.0%
e 418
25.0%
s 418
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1672
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 836
50.0%
e 418
25.0%
s 418
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1672
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 836
50.0%
e 418
25.0%
s 418
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 836
50.0%
e 418
25.0%
s 418
25.0%

Title
Categorical

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
Mr
243 
Miss
80 
Mrs
72 
Master
 
21
Other
 
2

Length

Max length6
Median length2
Mean length2.7703349
Min length2

Characters and Unicode

Total characters1158
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMr
2nd rowMrs
3rd rowMr
4th rowMr
5th rowMrs

Common Values

ValueCountFrequency (%)
Mr 243
58.1%
Miss 80
 
19.1%
Mrs 72
 
17.2%
Master 21
 
5.0%
Other 2
 
0.5%

Length

2024-03-23T16:04:33.596902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:33.742554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
mr 243
58.1%
miss 80
 
19.1%
mrs 72
 
17.2%
master 21
 
5.0%
other 2
 
0.5%

Most occurring characters

ValueCountFrequency (%)
M 416
35.9%
r 338
29.2%
s 253
21.8%
i 80
 
6.9%
t 23
 
2.0%
e 23
 
2.0%
a 21
 
1.8%
O 2
 
0.2%
h 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 740
63.9%
Uppercase Letter 418
36.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 338
45.7%
s 253
34.2%
i 80
 
10.8%
t 23
 
3.1%
e 23
 
3.1%
a 21
 
2.8%
h 2
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
M 416
99.5%
O 2
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1158
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 416
35.9%
r 338
29.2%
s 253
21.8%
i 80
 
6.9%
t 23
 
2.0%
e 23
 
2.0%
a 21
 
1.8%
O 2
 
0.2%
h 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 416
35.9%
r 338
29.2%
s 253
21.8%
i 80
 
6.9%
t 23
 
2.0%
e 23
 
2.0%
a 21
 
1.8%
O 2
 
0.2%
h 2
 
0.2%

CabinPrefix
Categorical

MISSING 

Distinct7
Distinct (%)1.7%
Missing13
Missing (%)3.1%
Memory size23.8 KiB
F
203 
E
63 
C
61 
G
39 
B
 
19
Other values (2)
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters405
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
F 203
48.6%
E 63
 
15.1%
C 61
 
14.6%
G 39
 
9.3%
B 19
 
4.5%
D 13
 
3.1%
A 7
 
1.7%
(Missing) 13
 
3.1%

Length

2024-03-23T16:04:33.878226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:34.023837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
f 203
50.1%
e 63
 
15.6%
c 61
 
15.1%
g 39
 
9.6%
b 19
 
4.7%
d 13
 
3.2%
a 7
 
1.7%

Most occurring characters

ValueCountFrequency (%)
F 203
50.1%
E 63
 
15.6%
C 61
 
15.1%
G 39
 
9.6%
B 19
 
4.7%
D 13
 
3.2%
A 7
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 405
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 203
50.1%
E 63
 
15.6%
C 61
 
15.1%
G 39
 
9.6%
B 19
 
4.7%
D 13
 
3.2%
A 7
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 405
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 203
50.1%
E 63
 
15.6%
C 61
 
15.1%
G 39
 
9.6%
B 19
 
4.7%
D 13
 
3.2%
A 7
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 203
50.1%
E 63
 
15.6%
C 61
 
15.1%
G 39
 
9.6%
B 19
 
4.7%
D 13
 
3.2%
A 7
 
1.7%

FamilySize
Real number (ℝ)

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8397129
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-23T16:04:34.174434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.519072
Coefficient of variation (CV)0.82571144
Kurtosis13.431226
Mean1.8397129
Median Absolute Deviation (MAD)0
Skewness3.1685425
Sum769
Variance2.3075798
MonotonicityNot monotonic
2024-03-23T16:04:34.301134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 253
60.5%
2 74
 
17.7%
3 57
 
13.6%
4 14
 
3.3%
5 7
 
1.7%
7 4
 
1.0%
11 4
 
1.0%
6 3
 
0.7%
8 2
 
0.5%
ValueCountFrequency (%)
1 253
60.5%
2 74
 
17.7%
3 57
 
13.6%
4 14
 
3.3%
5 7
 
1.7%
6 3
 
0.7%
7 4
 
1.0%
8 2
 
0.5%
11 4
 
1.0%
ValueCountFrequency (%)
11 4
 
1.0%
8 2
 
0.5%
7 4
 
1.0%
6 3
 
0.7%
5 7
 
1.7%
4 14
 
3.3%
3 57
 
13.6%
2 74
 
17.7%
1 253
60.5%
Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
Single
253 
Small Group
145 
Big Group
 
20

Length

Max length11
Median length6
Mean length7.8779904
Min length6

Characters and Unicode

Total characters3293
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSmall Group
3rd rowSingle
4th rowSingle
5th rowSmall Group

Common Values

ValueCountFrequency (%)
Single 253
60.5%
Small Group 145
34.7%
Big Group 20
 
4.8%

Length

2024-03-23T16:04:34.438728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:34.582384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
single 253
43.4%
group 165
28.3%
small 145
24.9%
big 20
 
3.4%

Most occurring characters

ValueCountFrequency (%)
l 543
16.5%
S 398
12.1%
i 273
8.3%
g 273
8.3%
n 253
 
7.7%
e 253
 
7.7%
165
 
5.0%
G 165
 
5.0%
r 165
 
5.0%
o 165
 
5.0%
Other values (5) 640
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2545
77.3%
Uppercase Letter 583
 
17.7%
Space Separator 165
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 543
21.3%
i 273
10.7%
g 273
10.7%
n 253
9.9%
e 253
9.9%
r 165
 
6.5%
o 165
 
6.5%
u 165
 
6.5%
p 165
 
6.5%
m 145
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
S 398
68.3%
G 165
28.3%
B 20
 
3.4%
Space Separator
ValueCountFrequency (%)
165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3128
95.0%
Common 165
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 543
17.4%
S 398
12.7%
i 273
8.7%
g 273
8.7%
n 253
8.1%
e 253
8.1%
G 165
 
5.3%
r 165
 
5.3%
o 165
 
5.3%
u 165
 
5.3%
Other values (4) 475
15.2%
Common
ValueCountFrequency (%)
165
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 543
16.5%
S 398
12.1%
i 273
8.3%
g 273
8.3%
n 253
 
7.7%
e 253
 
7.7%
165
 
5.0%
G 165
 
5.0%
r 165
 
5.0%
o 165
 
5.0%
Other values (5) 640
19.4%

IsAlone
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
Alone
253 
With Family
165 

Length

Max length11
Median length5
Mean length7.3684211
Min length5

Characters and Unicode

Total characters3080
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlone
2nd rowWith Family
3rd rowAlone
4th rowAlone
5th rowWith Family

Common Values

ValueCountFrequency (%)
Alone 253
60.5%
With Family 165
39.5%

Length

2024-03-23T16:04:34.726001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T16:04:34.872631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
alone 253
43.4%
with 165
28.3%
family 165
28.3%

Most occurring characters

ValueCountFrequency (%)
l 418
13.6%
i 330
10.7%
A 253
 
8.2%
o 253
 
8.2%
n 253
 
8.2%
e 253
 
8.2%
W 165
 
5.4%
t 165
 
5.4%
h 165
 
5.4%
165
 
5.4%
Other values (4) 660
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2332
75.7%
Uppercase Letter 583
 
18.9%
Space Separator 165
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 418
17.9%
i 330
14.2%
o 253
10.8%
n 253
10.8%
e 253
10.8%
t 165
 
7.1%
h 165
 
7.1%
a 165
 
7.1%
m 165
 
7.1%
y 165
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
A 253
43.4%
W 165
28.3%
F 165
28.3%
Space Separator
ValueCountFrequency (%)
165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2915
94.6%
Common 165
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 418
14.3%
i 330
11.3%
A 253
8.7%
o 253
8.7%
n 253
8.7%
e 253
8.7%
W 165
 
5.7%
t 165
 
5.7%
h 165
 
5.7%
F 165
 
5.7%
Other values (3) 495
17.0%
Common
ValueCountFrequency (%)
165
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 418
13.6%
i 330
10.7%
A 253
 
8.2%
o 253
 
8.2%
n 253
 
8.2%
e 253
 
8.2%
W 165
 
5.4%
t 165
 
5.4%
h 165
 
5.4%
165
 
5.4%
Other values (4) 660
21.4%

Interactions

2024-03-23T16:04:27.007489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:23.175735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:23.964625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:24.847265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:25.657101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:26.315368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:27.119192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:23.307383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:24.208974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:24.958967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:25.757864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:26.424050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:27.228930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:23.443020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:24.359572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:25.086627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:25.871528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:26.542734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:27.365535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:23.589628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:24.494242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:25.232272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:25.993202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:26.678369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:27.469255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:23.723304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:24.608904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:25.397827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:26.094930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:26.785117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:27.573977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:23.836967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:24.722632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:25.547394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:26.209623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-23T16:04:26.899779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Missing values

2024-03-23T16:04:27.745517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T16:04:28.058681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-23T16:04:28.251166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedDatasetNameTitleCabinPrefixFamilySizeTicketAppearancesIsAlone
0892NaN3Kelly, Mr. Jamesmale34.5003309117.8292NaNQtestMrF1SingleAlone
1893NaN3Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNStestMrsF2Small GroupWith Family
2894NaN2Myles, Mr. Thomas Francismale62.0002402769.6875NaNQtestMrF1SingleAlone
3895NaN3Wirz, Mr. Albertmale27.0003151548.6625NaNStestMrE1SingleAlone
4896NaN3Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNStestMrsE3Small GroupWith Family
5897NaN3Svensson, Mr. Johan Cervinmale14.00075389.2250NaNStestMrE1SingleAlone
6898NaN3Connolly, Miss. Katefemale30.0003309727.6292NaNQtestMissF1SingleAlone
7899NaN2Caldwell, Mr. Albert Francismale26.01124873829.0000NaNStestMrF3Small GroupWith Family
8900NaN3Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.00026577.2292NaNCtestMrsF1SingleAlone
9901NaN3Davies, Mr. John Samuelmale21.020A/4 4887124.1500NaNStestMrG3Small GroupWith Family
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedDatasetNameTitleCabinPrefixFamilySizeTicketAppearancesIsAlone
4081300NaN3Riordan, Miss. Johanna Hannah""femaleNaN003349157.7208NaNQtestMissF1SingleAlone
4091301NaN3Peacock, Miss. Treasteallfemale3.011SOTON/O.Q. 310131513.7750NaNStestMissE3Small GroupWith Family
4101302NaN3Naughton, Miss. HannahfemaleNaN003652377.7500NaNQtestMissF1SingleAlone
4111303NaN1Minahan, Mrs. William Edward (Lillian E Thorpe)female37.0101992890.0000C78QtestMrsC2Small GroupWith Family
4121304NaN3Henriksson, Miss. Jenny Lovisafemale28.0003470867.7750NaNStestMissF1SingleAlone
4131305NaN3Spector, Mr. WoolfmaleNaN00A.5. 32368.0500NaNStestMrE1SingleAlone
4141306NaN1Oliva y Ocana, Dona. Ferminafemale39.000PC 17758108.9000C105CtestMissC1SingleAlone
4151307NaN3Saether, Mr. Simon Sivertsenmale38.500SOTON/O.Q. 31012627.2500NaNStestMrF1SingleAlone
4161308NaN3Ware, Mr. FrederickmaleNaN003593098.0500NaNStestMrE1SingleAlone
4171309NaN3Peter, Master. Michael JmaleNaN11266822.3583NaNCtestMasterG3Small GroupWith Family